Numerous self-propelled robots for cleaning or treating floor areas are known and are commercially available. In principle, the intention is to treat the floor area as completely as possible in the shortest possible time. Random navigation methods are used in simple systems (for example EP 2287697 A2 by iRobot Corp.), which methods manage without creating or using a map of the environment in which the floor area to be treated is situated. That is to say, no local information relating to obstacles, floor area boundaries, cleaned/uncleaned areas, etc. is used. In combination with local movement strategies, only the direction of travel is (randomly) changed in the event of a collision with an obstacle. As a result, repeated cleaning of floor areas, for example, is accepted without being able to give a guarantee (in finite time) for complete cleaning of the floor area.
More complicated systems create a map of the environment for targeted path planning and targeted cleaning of the floor area using a SLAM algorithm (SLAM: “Simultaneous Localization and Mapping”). In this case, a map and the position of the robot in the map are determined using external sensors (laser range scanner, triangulation by means of a camera and laser, contact sensors, etc.) and inertial sensors (odometric sensors, acceleration sensors, etc.). In recent cleaning robots which use such a SLAM module, the map created is not permanent, that is to say a new map is created for each new cleaning operation (that is to say after the conclusion of a preceding cleaning operation).
In contrast to non-permanent maps, the use of permanently stored maps enables more efficient treatment operations since there is no need to repeatedly explore the environment. A treatment operation can therefore be calculated in advance. In this case, additional map-based information can be determined and reused (for example problem areas, heavily soiled areas, etc.). However, user-specific information, for example room designations, areas which require more intensive cleaning or blocked areas, the input of which would not be useful in non-permanent maps, can also be adopted. In U.S. Pat. No. 6,667,592 B2 by Intellibot, a stored/permanent map is used, for example, to assign (possibly different) functions (for example vacuuming, wiping) of individual sections to a map, which functions can then be autonomously executed by a cleaning device. In U.S. 2009/0182464 A1 by Samsung, the available map is broken down into sections which are then cleaned sequentially.
A basic prerequisite for robots which permanently store maps is that the robot can autonomously locate itself in the permanent map without or with only very limited prior knowledge of its actual position relative to the map. This ability is also referred to as global self-localization.
Such a method is described, for example, in “Active Global Localization for a Mobile Robot using Multiple Hypothesis Tracking”, IEEE Transactions on Robotics and Automation, 2001.
However, a self-localization process which is carried out according to such a method may last for a very long time, depending on the size and number of available maps. During this time, the robot is partially shut down for its actual task, for example approaching a destination, as a result of which the execution of tasks is delayed.
The object on which the invention is based is now to provide an autonomous robot which carries out a self-localization process as rarely as possible, in particular only when it proves to be necessary, in order to save time and energy for the actual execution of tasks.